Scalable appearance filtering for complex lighting effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Realistic rendering with materials that exhibit high-frequency spatial variation remains a challenge, as eliminating spatial and temporal aliasing requires prohibitively high sampling rates. Recent work has made the problem more tractable, however existing methods remain prohibitively expensive when using large environmental lights and/or (correctly filtered) global illumination. We present an appearance model with explicit high-frequency micro-normal variation, and a filtering approach that scales to multi-dimensional shading integrals. By combining a novel and compact half-vector histogram scheme with a directional basis expansion, we accurately compute the integral of filtered high-frequency reflectance over large lights with angularly varying emission. Our approach is scalable, rendering images indistinguishable from ground truth at over 10× the speed of the state-of-the-art and with only 15% the memory footprint. When filtering appearance with global illumination, we outperform the state-of-the-art by ~30×.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it